Aug 2026· IEEE Intelligent Systems· pp. 1-8· 0 citations· 25 references
Computer Science
TL;DR
This work systematically analyzes risks induced by expanding cognitive capabilities, following a three-level framework defined by their cognitive scope, from physical cognition to social cognition, and finally to self-referential cognition.
Abstract
Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their cognitive engagement raises critical concerns for human society that remain insufficiently studied. To address this gap, we systematically analyze risks induced by expanding cognitive capabilities, following a three-level framework defined by their cognitive scope, from physical cognition to social cognition, and finally to self-referential cognition. We study their potential risks to human agency, autonomy, and control capability, corresponding to each cognitive level. We finally propose strategies to mitigate these risks and enhance the controllability of agentic AI systems, ensuring their long-term safe development.
LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of anthropomorphic projection. We argue that this framing is mistaken. LLMs clearly differ from humans in important respec...
C. Sripada, Richard L. Lewis· arXiv.org· 0 citations
A taxonomy-driven survey of the major cognitive capability gaps that continue to constrain the development of Cognitive AI and outlines a conceptual Adaptive Cognitive Intelligence Architecture (ACIA), providing a foundation for future progress toward Cognitive AI and, ultimately, Artificial General Intelligence (AGI).
Taye Akinrele, Sindhuja Penchala, Noorbakhsh Amiri Golilarz et al.· 0 citations
A unified framework connecting empirical hallmarks of consciousness attribution to a structured risk taxonomy of Seemingly Conscious AI (SCAI), AI systems that exhibit hallmarks which elicit consciousness attribution from users is provided.
Ben Bariach, P. Schoenegger, M. Bhaskar et al.· AI and Ethics· 3 citations
ACE-Brain-0.5 is presented, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement, and SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector...
Zi-Yang Gong, Hao-Ming Gu, Ze-Hang Luo et al.· arXiv.org· 3 citations
Different capacities for mentalization across LLMs are demonstrated, and cognitive computational modeling is highlighted as a formal method for assessing comparative intelligence across humans and machines.
Aamir Sohail, Xintong Zhong, Arkady Konovalov et al.· 0 citations
If AI is to support human cognitive growth, design must move beyond answer provision and efficiency maximization toward the organization of productive human-AI relations: relations that challenge users’ initial assumptions while providing support appropriate to the task and the user's level of expertise.
Xiaokun Wu, Min Chen, Giancarlo Fortino· Big Data and Cognitive Compu...· 0 citations
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